rdiversity

Measurement and Partitioning of Similarity-Sensitive
Biodiversity

Provides a framework for the measurement and partitioning of
the (similarity-sensitive) biodiversity of a metacommunity and its
constituent subcommunities. Richard Reeve, et al. (2015)
.

rdiversity is a package for R based around a framework for measuring biodiversity using similarity-sensitive diversity measures. It provides functionality for measuring alpha, beta and gamma diversity of metacommunities (e.g. ecosystems) and their constituent subcommunities, where similarity may be defined as taxonomic, phenotypic, genetic, phylogenetic, functional, and so on. It uses the diversity framework described in the arXiv paper arXiv:1404.6520 (q-bio.QM), How to partition diversity.

This package has now reached a stable release and is cross-validated against our Julia package richardreeve/Diversity.jl, which is developed independently. Please raise an issue if you find any problems.

Installation

To install the latest development version of rdiversity, simply run the following from an R console:

install.packages("devtools")

devtools::install_github("boydorr/rdiversity")

Getting started

Before calculating diversity a metacommunity object must be created. This object contains all the information needed to calculate diversity.

# Load the package into R

library(rdiversity)

# Example population

pop <- data.frame(a=c(1,1,0),b=c(2,0,0),c=c(3,1,0))

# Create metacommunity object

meta <- metacommunity(pop)

The metacommunity() function takes two arguments, partition and similarity, and creates an object containing:

@type_abundance : the abundance of types within a population,

@similarity : the pair-wise similarity of types within a population,

@ordinariness : the ordinariness of types within a population,

@subcommunity_weights : the relative weights of subcommunities within a population,

@type_weights : the relative weights of types within a population, and

A metcommunity originating from a phylogeny will contain three additional slots:

@raw_abundance : the relative abundance of present-day species (where types are then considered to be historical species),

@raw_structure : the length of evolutionary history of each historical species

@parameters : parameters associated with historical species

Calculating diversity

First we need to calculate the low-level diversity component seperately, by passing a metacommunity object to the appropriate function; raw_alpha(), norm_alpha(), raw_beta(), norm_beta(), raw_rho(), norm_rho(), or raw_gamma().

# First, calculate the normalised subcommunity alpha component

component <- norm_alpha(meta)

Afterwhich, subdiv() or metadiv() are used to calculate subcommunity or metacommunity diversity, respectively (since both subcommunity and metacommunity diversity measures are transformations of the same low-level components, this is computationally more efficient).

# Then, calculate species richness

subdiv(component, 0)

# or the average species richness across the whole population

metadiv(component, 0)

# We can also generate a diversity profile (for a single diversity measure, or multiple measures of the same level) by calculating multiple q-values simultaneously

df <- subdiv(component, 0:30)

plot_diversity(df)

In some instances, it may be useful to calculate all subcommunity (or metacommunity) measures. In which case, a metacommunity object may be passed directly to subdiv() or metadiv():

# To calculate all subcommunity diversity measures

subdiv(meta, 0:2)

# To calculate all metacommunity diversity measures

metadiv(meta, 0:2)

Alternatively, if computational efficiency is not an issue, a single measure of diversity may be calculated directly by calling a wrapper function: